library(tidyverse)
## -- Attaching packages ------------------------ tidyverse 1.2.1 --
## v ggplot2 3.2.1     v purrr   0.3.2
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   0.8.3     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0
## -- Conflicts --------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(ggplot2)
library(readr)
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
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##     filter
## The following object is masked from 'package:graphics':
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##     layout
MetroPriceCut1 <- read_csv("MetroPriceCut1.csv")
## Parsed with column specification:
## cols(
##   Date = col_character(),
##   PriceCut = col_double(),
##   RegionID = col_double(),
##   RegionName = col_character(),
##   SizeRank = col_double()
## )
StatePriceCut1 <- read_csv("StatePriceCut1.csv")
## Parsed with column specification:
## cols(
##   Date = col_character(),
##   PriceCut = col_double(),
##   RegionID = col_double(),
##   RegionName = col_character(),
##   SizeRank = col_double()
## )
TopTierPC      <- read_csv("PriceCutTopTier.csv")
## Parsed with column specification:
## cols(
##   Date = col_character(),
##   PriceCut = col_double(),
##   RegionID = col_double(),
##   RegionName = col_character(),
##   SizeRank = col_double()
## )
MiddleTierPC   <- read_csv("PriceCutMiddleTier.csv")
## Parsed with column specification:
## cols(
##   Date = col_character(),
##   PriceCut = col_double(),
##   RegionID = col_double(),
##   RegionName = col_character(),
##   SizeRank = col_double()
## )
BottomTierPC   <- read_csv("PriceCutBottomTier.csv")
## Parsed with column specification:
## cols(
##   Date = col_character(),
##   PriceCut = col_double(),
##   RegionID = col_double(),
##   RegionName = col_character(),
##   SizeRank = col_double()
## )
g1 <- ggplot(MetroPriceCut1, aes(Date, PriceCut)) +
  geom_point(alpha = 0.1) +
  theme_minimal()

ggplotly(g1)
g2 <- ggplot(StatePriceCut1, aes(Date, PriceCut,
                                 color = RegionName)) +
  geom_point(alpha = 0.5) +
  theme_minimal()

ggplotly(g2)
g3 <- 
ggplot(TopTierPC, aes(Date, PriceCut,
                                 color = RegionName)) +
  geom_point(alpha = 0.2) +
  theme_minimal() +
  theme(legend.position = "none")
g4 <- ggplot(MiddleTierPC, aes(Date, PriceCut,
                                 color = RegionName)) +
  geom_point(alpha = 0.2) +
  theme_minimal() +
  theme(legend.position = "none")
g5 <- ggplot(BottomTierPC, aes(Date, PriceCut,
                                 color = RegionName)) +
  geom_point(alpha = 0.2) +
  theme_minimal() +
  theme(legend.position = "none")
ggplotly(g3)
ggplotly(g4)
ggplotly(g5)